Current state-of-the-art MTL approaches mainly focus on adaptively learning weights of different tasks in a mixture-of-experts structure, such as MMOE [1]} and PLE [2]}.
In such MTL structures, task weights change in different samples, which makes useful but not conflicting information to adaptively share among tasks.
Considering these MTL approaches have achieved promising performances in various scenarios, a simple way for reject inference might be directly applying them in training credit scoring models.
However, according to experiments in Sec. REF , we obtain very poor performances in default prediction on rejected samples.
This could be because some task weights are not well learned, since we have no observed default/non-default labels for rejected samples during model training.
In the feature distribution of rejected samples, there is no supervision for optimizing the task weights to control how much information is shared from the rejection/approval task to the default/non-default task.
Thus, existing MTL approaches fail in biased credit scoring data, and we propose RMT-Net to learn the task weights by a gating network based on rejection probabilities.
